Point scoring, widely used in criminology and other social sciences, is a simple way of predicting a binary response on the basis of binary explanatory variables. Like all statistical predictors they are liable to shrinkage, working less well on a validation sample than they appear to do on the original data. The paper examines the extent of shrinkage and proposes shrinkage-adjusted predictions. The related 'independence Bayes' method is also considered, and found to shrink more than the basic point scoring method. The results are applied to data from a cohort study in the development of delinquency
Objectives When developing a clinical prediction model, penalization techniques are recommended to a...
Parameter shrinkage is known to reduce fitting and prediction errors in linear models. When the vari...
In this paper a variety of shrinkage methods for estimating unknown population parameters has been c...
12 pages, 12 figuresInternational audienceThis paper presents a simple shrinkage estimator of rates ...
Contemporary statistical research frequently deals with problems involving a diverging number of par...
A simple shrinkage method is proposed to improve the performance of weighting estimators of the aver...
Contemporary statistical research frequently deals with problems involving a diverging number of par...
This paper shows how ridge regression and other shrinkage estimates can be used to improve the perfo...
True values, data, sample means, and a Bayesian hierarchical shrinkage estimates are shown. The shri...
It is common in the implementation of teacher accountability systems to use a procedure known as Emp...
Focusing on a single sample obtained randomly with replacement from a single population, this articl...
Regression analysis is a commonly used approach to modelling the relationships between dependent and...
In linear regression problems with many predictors, penalized regression techniques are often used t...
Objectives When developing a clinical prediction model, penalization techniques are recommended to ...
Logistic regression analysis may well be used to develop a predictive model for a dichotomous medica...
Objectives When developing a clinical prediction model, penalization techniques are recommended to a...
Parameter shrinkage is known to reduce fitting and prediction errors in linear models. When the vari...
In this paper a variety of shrinkage methods for estimating unknown population parameters has been c...
12 pages, 12 figuresInternational audienceThis paper presents a simple shrinkage estimator of rates ...
Contemporary statistical research frequently deals with problems involving a diverging number of par...
A simple shrinkage method is proposed to improve the performance of weighting estimators of the aver...
Contemporary statistical research frequently deals with problems involving a diverging number of par...
This paper shows how ridge regression and other shrinkage estimates can be used to improve the perfo...
True values, data, sample means, and a Bayesian hierarchical shrinkage estimates are shown. The shri...
It is common in the implementation of teacher accountability systems to use a procedure known as Emp...
Focusing on a single sample obtained randomly with replacement from a single population, this articl...
Regression analysis is a commonly used approach to modelling the relationships between dependent and...
In linear regression problems with many predictors, penalized regression techniques are often used t...
Objectives When developing a clinical prediction model, penalization techniques are recommended to ...
Logistic regression analysis may well be used to develop a predictive model for a dichotomous medica...
Objectives When developing a clinical prediction model, penalization techniques are recommended to a...
Parameter shrinkage is known to reduce fitting and prediction errors in linear models. When the vari...
In this paper a variety of shrinkage methods for estimating unknown population parameters has been c...